Data is often collected to monitor the operation of industrial machines. Such data collection may be used to diagnose problems, troubleshoot, trend operating changes, or otherwise record data points indicative of machine operation. A variety of data types may be collected, including temperature, vibration, and the like. The data collection may be continuous, i.e., using dedicated resources for individual machines or groups of machines. In other cases, data collection may be on-demand, for example, in routine checking and maintenance of the machines. In the latter case, mobile units may be provided that use sensors that are either permanently or temporarily coupled with the machine being measured.
Such on-demand data collection may, however, be costly in terms of time and resources. For example, if several machines are being checked using a mobile unit, complexity in the operation of the data collection unit may be multiplied and may require significant time allocation. Further, bulkiness of such units may hinder movement between machines being measured. However, a reduction in unit size may reduce functionality, such as the ability to retain measurements from previous operations, execute data collection/management software, etc. Complexity may also introduce the possibility of human error, and thus resources may be expended in training users to operate the units.
Furthermore, with mobile units, since they may capture data about several different machines in a session, care must be taken to establish the correct operating conditions to be monitored, associated with the correct machine, and with the correct operating constraints applied thereto, etc. Typically, this requires significant investments in training personnel responsible for collecting the data using the mobile units, and in software to manage the collected data and minimize error.